Webscraping
Extracting and Visualizing Stock Data
Description
Table of Contents
Define Graphing Function
Question 1: Use yfinance to Extract Stock Data
Question 2: Use Webscraping to Extract Tesla Revenue Data
Question 3: Use yfinance to Extract Stock Data
Question 4: Use Webscraping to Extract GME Revenue Data
Question 5: Plot Tesla Stock Graph
Question 6: Plot GameStop Stock Graph
About the Authors:
Change Log
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***Note***:- If you are working Locally using anaconda, please uncomment the following code and execute it.
Use the version as per your python version.
Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.
[57]:
!pip install yfinance
!pip install bs4
!pip install nbformat
Requirement already satisfied: yfinance in /opt/conda/lib/python3.12/site-packages (0.2.52) Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.3) Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.2) Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.32.3) Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.0.11) Requirement already satisfied: lxml>=4.9.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (5.3.0) Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.3.6) Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2024.2) Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.6) Requirement already satisfied: peewee>=3.16.2 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.17.8) Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.12.3) Requirement already satisfied: html5lib>=1.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (1.1) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5) Requirement already satisfied: six>=1.9 in /opt/conda/lib/python3.12/site-packages (from html5lib>=1.1->yfinance) (1.17.0) Requirement already satisfied: webencodings in /opt/conda/lib/python3.12/site-packages (from html5lib>=1.1->yfinance) (0.5.1) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2025.1) Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.4.1) Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2.3.0) Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2024.12.14) Requirement already satisfied: bs4 in /opt/conda/lib/python3.12/site-packages (0.0.2) Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.12/site-packages (from bs4) (4.12.3) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4->bs4) (2.5) Requirement already satisfied: nbformat in /opt/conda/lib/python3.12/site-packages (5.10.4) Requirement already satisfied: fastjsonschema>=2.15 in /opt/conda/lib/python3.12/site-packages (from nbformat) (2.21.1) Requirement already satisfied: jsonschema>=2.6 in /opt/conda/lib/python3.12/site-packages (from nbformat) (4.23.0) Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.7.2) Requirement already satisfied: traitlets>=5.1 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.14.3) Requirement already satisfied: attrs>=22.2.0 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (25.1.0) Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (2024.10.1) Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.36.2) Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.22.3) Requirement already satisfied: platformdirs>=2.5 in /opt/conda/lib/python3.12/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.6) Requirement already satisfied: typing-extensions>=4.4.0 in /opt/conda/lib/python3.12/site-packages (from referencing>=0.28.4->jsonschema>=2.6->nbformat) (4.12.2)
[58]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.
In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.
[62]:
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2010-06-29 00:00:00-04:00 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0.0 | 0.0 |
| 2010-06-30 00:00:00-04:00 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0.0 | 0.0 |
| 2010-07-01 00:00:00-04:00 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0.0 | 0.0 |
| 2010-07-02 00:00:00-04:00 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0.0 | 0.0 |
| 2010-07-06 00:00:00-04:00 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0.0 | 0.0 |
[63]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 00:00:00-04:00 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0.0 | 0.0 |
| 1 | 2010-06-30 00:00:00-04:00 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0.0 | 0.0 |
| 2 | 2010-07-01 00:00:00-04:00 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0.0 | 0.0 |
| 3 | 2010-07-02 00:00:00-04:00 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0.0 | 0.0 |
| 4 | 2010-07-06 00:00:00-04:00 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0.0 | 0.0 |
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<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<h2 style="margin-left:0px; font-weight:600; color:#444;">Tesla Revenue 2010-2022 | TSLA</h2>
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<span style="color:#444; line-height: 1.8;">Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<th colspan=2 style="text-align:center">Tesla Annual Revenue<br /><span style="font-size:14px;">(Millions of US $)</span></th>
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</thead>
<tbody>
<tr>
<td style="text-align:center">2021</td>
<td style="text-align:center">$53,823</td>
</tr>
<tr>
<td style="text-align:center">2020</td>
<td style="text-align:center">$31,536</td>
</tr>
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<td style="text-align:center">2019</td>
<td style="text-align:center">$24,578</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">$21,461</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">$11,759</td>
</tr>
<tr>
<td style="text-align:center">2016</td>
<td style="text-align:center">$7,000</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">$4,046</td>
</tr>
<tr>
<td style="text-align:center">2014</td>
<td style="text-align:center">$3,198</td>
</tr>
<tr>
<td style="text-align:center">2013</td>
<td style="text-align:center">$2,013</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">$413</td>
</tr>
<tr>
<td style="text-align:center">2011</td>
<td style="text-align:center">$204</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">$117</td>
</tr>
<tr>
<td style="text-align:center">2009</td>
<td style="text-align:center">$112</td>
</tr>
</tbody>
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<table class="historical_data_table table">
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<tr>
<th colspan=2 style="text-align:center">Tesla Quarterly Revenue<br /><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2022-09-30</td>
<td style="text-align:center">$21,454</td>
</tr>
<tr>
<td style="text-align:center">2022-06-30</td>
<td style="text-align:center">$16,934</td>
</tr>
<tr>
<td style="text-align:center">2022-03-31</td>
<td style="text-align:center">$18,756</td>
</tr>
<tr>
<td style="text-align:center">2021-12-31</td>
<td style="text-align:center">$17,719</td>
</tr>
<tr>
<td style="text-align:center">2021-09-30</td>
<td style="text-align:center">$13,757</td>
</tr>
<tr>
<td style="text-align:center">2021-06-30</td>
<td style="text-align:center">$11,958</td>
</tr>
<tr>
<td style="text-align:center">2021-03-31</td>
<td style="text-align:center">$10,389</td>
</tr>
<tr>
<td style="text-align:center">2020-12-31</td>
<td style="text-align:center">$10,744</td>
</tr>
<tr>
<td style="text-align:center">2020-09-30</td>
<td style="text-align:center">$8,771</td>
</tr>
<tr>
<td style="text-align:center">2020-06-30</td>
<td style="text-align:center">$6,036</td>
</tr>
<tr>
<td style="text-align:center">2020-03-31</td>
<td style="text-align:center">$5,985</td>
</tr>
<tr>
<td style="text-align:center">2019-12-31</td>
<td style="text-align:center">$7,384</td>
</tr>
<tr>
<td style="text-align:center">2019-09-30</td>
<td style="text-align:center">$6,303</td>
</tr>
<tr>
<td style="text-align:center">2019-06-30</td>
<td style="text-align:center">$6,350</td>
</tr>
<tr>
<td style="text-align:center">2019-03-31</td>
<td style="text-align:center">$4,541</td>
</tr>
<tr>
<td style="text-align:center">2018-12-31</td>
<td style="text-align:center">$7,226</td>
</tr>
<tr>
<td style="text-align:center">2018-09-30</td>
<td style="text-align:center">$6,824</td>
</tr>
<tr>
<td style="text-align:center">2018-06-30</td>
<td style="text-align:center">$4,002</td>
</tr>
<tr>
<td style="text-align:center">2018-03-31</td>
<td style="text-align:center">$3,409</td>
</tr>
<tr>
<td style="text-align:center">2017-12-31</td>
<td style="text-align:center">$3,288</td>
</tr>
<tr>
<td style="text-align:center">2017-09-30</td>
<td style="text-align:center">$2,985</td>
</tr>
<tr>
<td style="text-align:center">2017-06-30</td>
<td style="text-align:center">$2,790</td>
</tr>
<tr>
<td style="text-align:center">2017-03-31</td>
<td style="text-align:center">$2,696</td>
</tr>
<tr>
<td style="text-align:center">2016-12-31</td>
<td style="text-align:center">$2,285</td>
</tr>
<tr>
<td style="text-align:center">2016-09-30</td>
<td style="text-align:center">$2,298</td>
</tr>
<tr>
<td style="text-align:center">2016-06-30</td>
<td style="text-align:center">$1,270</td>
</tr>
<tr>
<td style="text-align:center">2016-03-31</td>
<td style="text-align:center">$1,147</td>
</tr>
<tr>
<td style="text-align:center">2015-12-31</td>
<td style="text-align:center">$1,214</td>
</tr>
<tr>
<td style="text-align:center">2015-09-30</td>
<td style="text-align:center">$937</td>
</tr>
<tr>
<td style="text-align:center">2015-06-30</td>
<td style="text-align:center">$955</td>
</tr>
<tr>
<td style="text-align:center">2015-03-31</td>
<td style="text-align:center">$940</td>
</tr>
<tr>
<td style="text-align:center">2014-12-31</td>
<td style="text-align:center">$957</td>
</tr>
<tr>
<td style="text-align:center">2014-09-30</td>
<td style="text-align:center">$852</td>
</tr>
<tr>
<td style="text-align:center">2014-06-30</td>
<td style="text-align:center">$769</td>
</tr>
<tr>
<td style="text-align:center">2014-03-31</td>
<td style="text-align:center">$621</td>
</tr>
<tr>
<td style="text-align:center">2013-12-31</td>
<td style="text-align:center">$615</td>
</tr>
<tr>
<td style="text-align:center">2013-09-30</td>
<td style="text-align:center">$431</td>
</tr>
<tr>
<td style="text-align:center">2013-06-30</td>
<td style="text-align:center">$405</td>
</tr>
<tr>
<td style="text-align:center">2013-03-31</td>
<td style="text-align:center">$562</td>
</tr>
<tr>
<td style="text-align:center">2012-12-31</td>
<td style="text-align:center">$306</td>
</tr>
<tr>
<td style="text-align:center">2012-09-30</td>
<td style="text-align:center">$50</td>
</tr>
<tr>
<td style="text-align:center">2012-06-30</td>
<td style="text-align:center">$27</td>
</tr>
<tr>
<td style="text-align:center">2012-03-31</td>
<td style="text-align:center">$30</td>
</tr>
<tr>
<td style="text-align:center">2011-12-31</td>
<td style="text-align:center">$39</td>
</tr>
<tr>
<td style="text-align:center">2011-09-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-06-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-03-31</td>
<td style="text-align:center">$49</td>
</tr>
<tr>
<td style="text-align:center">2010-12-31</td>
<td style="text-align:center">$36</td>
</tr>
<tr>
<td style="text-align:center">2010-09-30</td>
<td style="text-align:center">$31</td>
</tr>
<tr>
<td style="text-align:center">2010-06-30</td>
<td style="text-align:center">$28</td>
</tr>
<tr>
<td style="text-align:center">2010-03-31</td>
<td style="text-align:center">$21</td>
</tr>
<tr>
<td style="text-align:center">2009-12-31</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">2009-09-30</td>
<td style="text-align:center">$46</td>
</tr>
<tr>
<td style="text-align:center">2009-06-30</td>
<td style="text-align:center">$27</td>
</tr>
</tbody>
</table>
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<th style="text-align:center">Sector</th>
<th style="text-align:center">Industry</th>
<th style="text-align:center">Market Cap</th>
<th style="text-align:center">Revenue</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><a href='https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks'>Auto/Tires/Trucks</a></td>
<td style="text-align:center"><a href='https://www.macrotrends.net/stocks/industry/7/'>Auto Manufacturers - Domestic</a></td>
<td style="text-align:center">$549.575B</td>
<td style="text-align:center">$53.823B</td>
</tr>
<tr>
<td colspan="4" style="padding:15px;">
<span>Tesla is the market leader in battery-powered electric car sales in the United States, with roughly 70% market share. The company's flagship Model 3 is the best-selling EV model in the United States. Tesla, which has managed to garner the reputation of a gold standard over the years, is now a far bigger entity that what it started off since its IPO in 2010, with its market cap crossing $1 trillion for the first time in October 2021.? The EV king's market capitalization is more than the combined value of legacy automakers including Toyota, Volkswagen, Daimler, General Motors and Ford.Over the years, Tesla has shifted from developing niche products for affluent buyers to making more affordable EVs for the masses. The firm's three-pronged business model approach of direct sales, servicing, and charging its EVs sets it apart from other carmakers. Tesla, which is touted as the clean energy revolutionary automaker, is much more than just a car manufacturer.</span>
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<th style="text-align:center; width:20%;">PE Ratio</th>
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<td style="text-align:left"><a href='/stocks/charts/GM/general-motors/revenue'>General Motors (GM)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$53.930B</td>
<td style="text-align:center">5.56</td>
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<td style="text-align:left"><a href='/stocks/charts/F/ford-motor/revenue'>Ford Motor (F)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$52.668B</td>
<td style="text-align:center">8.09</td>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$6.762B</td>
<td style="text-align:center">9.56</td>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$6.267B</td>
<td style="text-align:center">11.86</td>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$5.134B</td>
<td style="text-align:center">16.40</td>
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<td style="text-align:left"><a href='/stocks/charts/FSR/fisker/revenue'>Fisker (FSR)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$2.261B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href='/stocks/charts/LEV/lion-electric/revenue'>Lion Electric (LEV)</a></td>
<td style="text-align:center">Canada</td>
<td style="text-align:center">$0.551B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href='/stocks/charts/VLTA/volta/revenue'>Volta (VLTA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.071B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href='/stocks/charts/BRDS/bird-global/revenue'>Bird Global (BRDS)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.054B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href='/stocks/charts/ZEV/lightning-emotors/revenue'>Lightning EMotors (ZEV)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.043B</td>
<td style="text-align:center">0.00</td>
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[69]:
| Date | Revenue | |
|---|---|---|
| 8 | 2013 | $2,013 |
| 9 | 2012 | $413 |
| 10 | 2011 | $204 |
| 11 | 2010 | $117 |
| 12 | 2009 | $112 |
[70]:
| Date | Revenue | |
|---|---|---|
| 8 | 2013 | $2,013 |
| 9 | 2012 | $413 |
| 10 | 2011 | $204 |
| 11 | 2010 | $117 |
| 12 | 2009 | $112 |
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.12/site-packages (3.10.0) Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (1.3.1) Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (4.55.8) Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (1.4.8) Requirement already satisfied: numpy>=1.23 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (2.2.2) Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (24.2) Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (11.1.0) Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (3.2.1) Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0) Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)
Open High Low Close Volume \
Date
2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000
2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600
2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600
2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578048 1.607504 7410400
2002-02-20 00:00:00-05:00 1.615921 1.662210 1.603296 1.662210 6892800
Dividends Stock Splits
Date
2002-02-13 00:00:00-05:00 0.0 0.0
2002-02-14 00:00:00-05:00 0.0 0.0
2002-02-15 00:00:00-05:00 0.0 0.0
2002-02-19 00:00:00-05:00 0.0 0.0
2002-02-20 00:00:00-05:00 0.0 0.0
Date Open High Low Close Volume \ 0 2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000 1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600 3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578048 1.607504 7410400 4 2002-02-20 00:00:00-05:00 1.615921 1.662210 1.603296 1.662210 6892800 Dividends Stock Splits 0 0.0 0.0 1 0.0 0.0 2 0.0 0.0 3 0.0 0.0 4 0.0 0.0
<!DOCTYPE html> <!-- saved from url=(0105)https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue --> <html class=" js flexbox canvas canvastext webgl no-touch geolocation postmessage websqldatabase indexeddb hashchange history draganddrop websockets rgba hsla multiplebgs backgroundsize borderimage borderradius boxshadow textshadow opacity cssanimations csscolumns cssgradients cssreflections csstransforms csstransforms3d csstransitions fontface g
<!DOCTYPE html> <!-- saved from url=(0105)https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue --> <html class="js flexbox canvas canvastext webgl no-touch geolocation postmessage websqldatabase indexeddb hashchange history draganddrop websockets rgba hsla multiplebgs backgroundsize borderimage borderradius boxshadow textshadow opacity cssanimations csscolumns cssgradients cssreflections csstransforms csstransforms3d csstransitions fontface ge
Date Revenue 0 2020-04-30 1021 1 2020-01-31 2194 2 2019-10-31 1439 3 2019-07-31 1286 4 2019-04-30 1548
[80]:
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[98], line 1 ----> 1 make_graph(tesla_data, tesla_revenue, tesla) Cell In[97], line 3, in make_graph(stock_data, revenue_data, stock) 1 def make_graph(stock_data, revenue_data, stock): 2 fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3) ----> 3 stock_data_specific = stock_data[stock_data.Date <= '2021-06-14'] 4 revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30'] 5 fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1) AttributeError: 'str' object has no attribute 'Date'
Error: stock_data and revenue_data must be dataframes
Error: stock_data and revenue_data must be dataframes
/tmp/ipykernel_132/1106259432.py:14: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipykernel_132/1106259432.py:15: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
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